DataVisor UML Enterprise

Find sophisticated bad actors with unparalleled accuracy

DataVisor UML Detection is the industry’s first production-grade UML unsupervised machine learning fraud detection solution that is capable of processing all events and account activities simultaneously to analyze the patterns across hundreds of millions of accounts to detect suspicious connections between malicious accounts.

Requires no labels or training data

Does not rely on known labels of past attacks and finds previously unknown and emerging attacks

Predicts attacks at early stages

Identifies incubating accounts days to months before they conduct fraudulent activities

Uncovers entire crime ring

Captures the whole crime ring at high precision by identifying the subtle correlations across accounts

Discover hidden connections across millions of accounts

Profile Information

Demographic information associated with an account, usually provided at the account application or user registration time. It may include income range, gender, and address.

Relationships Between Accounts

Interactions and relationships between different accounts, e.g. one account sending money to other accounts that are friends or contacts.

Content and Metadata

Text and pictures generated by an account, such as comments, profile photos, and phone call records.

Behaviours and Activities

What the account has done and when. For example, payment events which include a timestamp, payment amount and method.

Origins and Digital Fingerprints

Information describing the access methods of an account, including its device types and version, browser information, IP.

Access all components of the DataVisor Platform

Unsupervised Machine Learning Engine

Predict new, unknown threats without labels or training data by analyzing hundreds of millions of accounts and events simultaneously using the industry’s most advanced unsupervised learning technology.